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PadChest: A large chest x-ray image dataset with multi-label annotated reports.

Aurelia Bustos1, Antonio Pertusa1, Jose-Maria Salinas2

  • 1Department of Software and Computing Systems, University Institute for Computing Research, University of Alicante, Spain.

Medical Image Analysis
|September 3, 2020
PubMed
Summary

We introduce PadChest, a large chest X-ray dataset with over 160,000 images and associated reports. This resource aids automated medical image analysis and includes Spanish reports, a novel feature for public datasets.

Keywords:
Anatomical locationsDeep neural networksDifferential diagnosesRadiographic findingsX-Ray image dataset

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Natural Language Processing for Clinical Data

Background:

  • Large-scale, high-resolution chest X-ray datasets are crucial for developing advanced medical image analysis tools.
  • Existing public datasets often lack comprehensive associated reports or are limited in language diversity.

Purpose of the Study:

  • To introduce PadChest, a novel, large-scale, high-resolution chest X-ray dataset with detailed radiologist reports.
  • To facilitate automated exploration and analysis of medical images and their associated textual interpretations.
  • To provide a valuable resource for training supervised models in medical image analysis, particularly for Spanish-language reports.

Main Methods:

  • Compilation of over 160,000 chest X-ray images from 67,000 patients (2009-2017).
  • Annotation of reports with 174 radiographic findings, 19 differential diagnoses, and 104 anatomic locations, mapped to Unified Medical Language System (UMLS) terminology.
  • Hybrid annotation approach: 27% manual by physicians, 73% by a supervised recurrent neural network with attention mechanisms.
  • Validation of generated labels on an independent test set, achieving a 0.93 Micro-F1 score.

Main Results:

  • Creation of PadChest, one of the largest publicly available chest X-ray datasets.
  • The dataset includes diverse views, image acquisition details, patient demographics, and comprehensive report annotations.
  • The automated labeling method achieved high accuracy (0.93 Micro-F1 score) in validating radiographic findings.
  • PadChest is the first public dataset of its kind to include radiographic reports in Spanish.

Conclusions:

  • PadChest represents a significant advancement in the availability of large-scale, annotated medical imaging data.
  • The dataset's comprehensive nature and Spanish language reports offer unique opportunities for research in medical AI.
  • This resource is expected to accelerate the development of supervised models for automated analysis of chest radiographs and reports.